1. Technical Field
The present invention relates to the processing of images, and more particularly the images obtained after data compression. In the context of processing digital images, each image is represented by a number of pixels per row and a number of pixels per column, with these integers varying as a function of the size of the image formats.
2. Description of the Related Art
The application of certain types of image processing, and more particularly the application of image processing for the purposes of compressing the digital data relating to the images, especially when DCT (“Discrete Cosine Transform”) is used, as is the case in MPEG (“Moving Picture Experts Group”) processing for example, induces artifacts in the images so processed, meaning noise in color or in movement. As a result, the images obtained after such compression may be of poor quality.
In particular, it is possible to see blocks of pixels presenting poor color uniformity from block to block. These uniform blocks of pixels appear to be delimited by respective block boundaries forming an artificial partitioning of the image which can be detected by the human eye and which can thus represent a first type of image noise.
As a general rule, the pixels of an image are processed in blocks of 8×8 pixels during compression. Under these conditions, each block of 8×8 pixels can present the same color level.
Such effects at the block level can be detected by the human eye. The greater the number of visible block boundaries present in the image, the lower the level of the image quality.
Certain algorithms known to a person having ordinary skill in the art aim to determine an image quality level based on the number of visible block boundaries which are present in an image.
The document “A Locally Adaptive Algorithm for Measuring Blocking Artifacts in Images and Videos” by F. Pan, X. Lin, S. Rahardja, W. Lin, E. Ong, S. Yao, Z. Lu and X. Yang describes an algorithm for measuring such noise so as to provide quality metrics for an image. The document proposes determining discontinuities between two blocks of adjacent pixels, meaning the presence of a visible block boundary, on the basis of a value Bh which incorporates the weighted difference between the values of adjacent pixels on the boundary between the two blocks in question, for the width of these two blocks of pixels.
A value of Bh which is less than 1 indicates there is no visible block boundary between the two blocks in question. However, a value of Bh equal to 10 indicates a strong discontinuity between the blocks of pixels and therefore a clearly visible block boundary.
Such a value is determined for a vertical block boundary Bv and for a horizontal block boundary Bh. Then an estimated value BBLK of the blockiness of a block of pixels in the image is determined from the mean of the values for a horizontal block boundary Bh and for a vertical block boundary Bv.
Image compression can also result in a second type of image noise, consisting of producing an image comprising multiple adjacent blocks of pixels which all present the same uniformity. In such a case, the human eye can then detect an artificial uniform zone, or uniform distortion zone, in the image, which can be more or less large. Such a phenomenon results in image noise and therefore detracts from the quality of the image obtained after data compression. In this case, no block boundary is visible between two blocks of pixels 8×8 in size, because the blocks of adjacent pixels present the same color level.
The document “A Locally Adaptive Algorithm for Measuring Blocking Artifacts in Images and Videos” cited above proposes detecting this second type of image noise on the basis of a value ZBLK which, for a vertical block boundary takes into account the differences in pixel values relative to the 4 columns of pixels to the right of the block boundary in question and also relative to the 4 columns of pixels to the left of this same block boundary, and for a horizontal block boundary takes into account the differences in pixel values relative to 4 rows of pixels above the block boundary in question and also relative to the 4 rows of pixels below this same block boundary.
The document therefore provides a method for determining a value BBLK relating to the artificial discontinuities in an image and a relative value ZBLK relating to the uniform distortion zones in an image.
This allows estimating an image quality level QIMAGE from these two values BBLK and ZBLK.
However, such a method involves complex calculations based in particular on the determination of weighting coefficients and scaling factors.
In addition, such a method for detecting the two types of noise represented by the effect relating to the artificial discontinuities and by the effect relating to the uniform distortion zones, may not be relevant in certain images. Such is the case when the image in question represents text. In fact, when the image obtained after compression remains faithful to an original image which represents text, by applying such a method it is possible to conclude that the image presents noise of the second type induced by image processing, although the obtained image is actually of high quality.